Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations966400
Missing cells0
Missing cells (%)0.0%
Duplicate rows3158
Duplicate rows (%)0.3%
Total size in memory667.1 MiB
Average record size in memory723.8 B

Variable types

Numeric10
DateTime2
Categorical8
Text4

Alerts

Dataset has 3158 (0.3%) duplicate rowsDuplicates
CATEGORY is highly overall correlated with SUBCATEGORYHigh correlation
DAY_OF_YEAR is highly overall correlated with MONTH and 3 other fieldsHigh correlation
MNTH_CODE is highly overall correlated with QUARTER and 1 other fieldsHigh correlation
MONTH is highly overall correlated with DAY_OF_YEAR and 3 other fieldsHigh correlation
OC_CODE is highly overall correlated with DAY_OF_YEAR and 3 other fieldsHigh correlation
QUARTER is highly overall correlated with DAY_OF_YEAR and 4 other fieldsHigh correlation
SALES_PTR_VALUE is highly overall correlated with SALES_VALUE and 1 other fieldsHigh correlation
SALES_VALUE is highly overall correlated with SALES_PTR_VALUE and 1 other fieldsHigh correlation
SALES_VOLUME is highly overall correlated with SALES_PTR_VALUE and 1 other fieldsHigh correlation
SUBCATEGORY is highly overall correlated with CATEGORYHigh correlation
YEAR is highly overall correlated with DAY_OF_YEAR and 4 other fieldsHigh correlation
SALES_VALUE is highly skewed (γ1 = 33.86988697)Skewed
SALES_UNITS is highly skewed (γ1 = 57.47378806)Skewed
SALES_VOLUME is highly skewed (γ1 = 34.22440987)Skewed
SALES_PTR_VALUE is highly skewed (γ1 = 32.65774068)Skewed
DAY_OF_WEEK has 102473 (10.6%) zerosZeros

Reproduction

Analysis started2024-09-26 14:43:16.624555
Analysis finished2024-09-26 14:44:14.558505
Duration57.93 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

MNTH_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202372.88
Minimum202309
Maximum202408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:14.639828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum202309
5-th percentile202309
Q1202312
median202403
Q3202406
95-th percentile202408
Maximum202408
Range99
Interquartile range (IQR)94

Descriptive statistics

Standard deviation44.525843
Coefficient of variation (CV)0.00022001883
Kurtosis-1.5218993
Mean202372.88
Median Absolute Deviation (MAD)4
Skewness-0.68520023
Sum1.9557315 × 1011
Variance1982.5507
MonotonicityNot monotonic
2024-09-26T14:44:14.783585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
202406 102585
10.6%
202309 96453
10.0%
202312 89583
9.3%
202403 84599
8.8%
202407 83813
8.7%
202408 74960
7.8%
202404 74939
7.8%
202402 74173
7.7%
202311 73399
7.6%
202401 73094
7.6%
Other values (2) 138802
14.4%
ValueCountFrequency (%)
202309 96453
10.0%
202310 66211
6.9%
202311 73399
7.6%
202312 89583
9.3%
202401 73094
7.6%
202402 74173
7.7%
202403 84599
8.8%
202404 74939
7.8%
202405 72591
7.5%
202406 102585
10.6%
ValueCountFrequency (%)
202408 74960
7.8%
202407 83813
8.7%
202406 102585
10.6%
202405 72591
7.5%
202404 74939
7.8%
202403 84599
8.8%
202402 74173
7.7%
202401 73094
7.6%
202312 89583
9.3%
202311 73399
7.6%
Distinct303
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
Minimum2023-08-29 00:00:00
Maximum2024-08-27 00:00:00
2024-09-26T14:44:14.942030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:15.129087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
Minimum2023-08-28 00:00:00
Maximum2024-07-31 00:00:00
2024-09-26T14:44:15.296529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:15.654613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

SALES_VALUE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8242
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.41745
Minimum2.86
Maximum145728.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-26T14:44:15.826663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile53.57
Q1140
median192.24
Q3450
95-th percentile1537.89
Maximum145728.12
Range145725.26
Interquartile range (IQR)310

Descriptive statistics

Standard deviation1053.3556
Coefficient of variation (CV)2.3595754
Kurtosis2647.1191
Mean446.41745
Median Absolute Deviation (MAD)87.76
Skewness33.869887
Sum4.3141783 × 108
Variance1109558.1
MonotonicityNot monotonic
2024-09-26T14:44:16.023773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.86 106972
 
11.1%
53.57 42760
 
4.4%
140 39597
 
4.1%
138.57 30170
 
3.1%
107.14 28309
 
2.9%
137.14 24945
 
2.6%
142.84 21903
 
2.3%
280 12096
 
1.3%
131.06 10428
 
1.1%
163.64 10138
 
1.0%
Other values (8232) 639082
66.1%
ValueCountFrequency (%)
2.86 2
 
< 0.1%
4.46 42
 
< 0.1%
7.81 1
 
< 0.1%
8.57 10
 
< 0.1%
8.65 1
 
< 0.1%
8.66 13
 
< 0.1%
8.75 16
 
< 0.1%
8.92 1
 
< 0.1%
8.93 254
< 0.1%
13.39 84
 
< 0.1%
ValueCountFrequency (%)
145728.12 1
< 0.1%
144803.75 1
< 0.1%
118027.64 1
< 0.1%
117676.65 1
< 0.1%
114606.54 1
< 0.1%
112931 1
< 0.1%
104407.27 1
< 0.1%
103488 1
< 0.1%
96727.27 1
< 0.1%
95594.54 1
< 0.1%

SALES_UNITS
Real number (ℝ)

SKEWED 

Distinct359
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.696476
Minimum1
Maximum10240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-09-26T14:44:16.227808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q316
95-th percentile32
Maximum10240
Range10239
Interquartile range (IQR)14

Descriptive statistics

Standard deviation40.761336
Coefficient of variation (CV)3.210445
Kurtosis7131.9482
Mean12.696476
Median Absolute Deviation (MAD)5
Skewness57.473788
Sum12269874
Variance1661.4865
MonotonicityNot monotonic
2024-09-26T14:44:16.423403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 260404
26.9%
1 131257
13.6%
2 122969
12.7%
3 119386
12.4%
6 72771
 
7.5%
12 71214
 
7.4%
32 57991
 
6.0%
4 34619
 
3.6%
24 25479
 
2.6%
8 17878
 
1.8%
Other values (349) 52432
 
5.4%
ValueCountFrequency (%)
1 131257
13.6%
2 122969
12.7%
3 119386
12.4%
4 34619
 
3.6%
5 5173
 
0.5%
6 72771
7.5%
7 579
 
0.1%
8 17878
 
1.8%
9 601
 
0.1%
10 2070
 
0.2%
ValueCountFrequency (%)
10240 1
 
< 0.1%
6000 1
 
< 0.1%
5120 2
< 0.1%
4800 1
 
< 0.1%
4388 1
 
< 0.1%
3840 1
 
< 0.1%
3600 3
< 0.1%
3500 1
 
< 0.1%
3360 1
 
< 0.1%
3200 1
 
< 0.1%

SALES_VOLUME
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1581
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00093144967
Minimum1.1 × 10-5
Maximum0.2755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-26T14:44:16.610180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 10-5
5-th percentile0.000144
Q10.000368
median0.000448
Q30.0009
95-th percentile0.00286
Maximum0.2755
Range0.275489
Interquartile range (IQR)0.000532

Descriptive statistics

Standard deviation0.0020629814
Coefficient of variation (CV)2.2148071
Kurtosis2628.5773
Mean0.00093144967
Median Absolute Deviation (MAD)0.000198
Skewness34.22441
Sum900.15296
Variance4.2558922 × 10-6
MonotonicityNot monotonic
2024-09-26T14:44:16.793424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000384 67198
 
7.0%
0.0004 63048
 
6.5%
0.000416 44261
 
4.6%
0.0005 32689
 
3.4%
0.000144 32367
 
3.3%
0.0003 25749
 
2.7%
0.00075 24511
 
2.5%
0.000368 22589
 
2.3%
0.000272 21633
 
2.2%
0.000132 20296
 
2.1%
Other values (1571) 612059
63.3%
ValueCountFrequency (%)
1.1 × 10-56
 
< 0.1%
1.2 × 10-536
 
< 0.1%
1.7 × 10-517
 
< 0.1%
1.8 × 10-56
 
< 0.1%
2 × 10-57
 
< 0.1%
2.2 × 10-522
 
< 0.1%
2.3 × 10-533
 
< 0.1%
2.4 × 10-5106
< 0.1%
2.5 × 10-547
< 0.1%
2.6 × 10-550
< 0.1%
ValueCountFrequency (%)
0.2755 1
 
< 0.1%
0.256 1
 
< 0.1%
0.2464 1
 
< 0.1%
0.242 1
 
< 0.1%
0.2375 1
 
< 0.1%
0.219 1
 
< 0.1%
0.21 1
 
< 0.1%
0.2097 1
 
< 0.1%
0.2095 1
 
< 0.1%
0.192 3
< 0.1%

SALES_PTR_VALUE
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2024
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.81468
Minimum1.7857143
Maximum151800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2024-09-26T14:44:16.939250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7857143
5-th percentile53.571429
Q1142.85714
median198.18182
Q3450
95-th percentile1585.4545
Maximum151800
Range151798.21
Interquartile range (IQR)307.14286

Descriptive statistics

Standard deviation1101.7691
Coefficient of variation (CV)2.4065832
Kurtosis2447.2998
Mean457.81468
Median Absolute Deviation (MAD)91.038961
Skewness32.657741
Sum4.4243211 × 108
Variance1213895.2
MonotonicityNot monotonic
2024-09-26T14:44:17.118360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.8571429 239443
24.8%
53.57142857 52969
 
5.5%
285.7142857 50215
 
5.2%
107.1428571 33816
 
3.5%
163.6363636 10338
 
1.1%
313.6363636 9855
 
1.0%
336.3636364 9789
 
1.0%
270 9751
 
1.0%
104.5454545 9638
 
1.0%
209.0909091 9229
 
1.0%
Other values (2014) 531357
55.0%
ValueCountFrequency (%)
1.785714286 1
 
< 0.1%
4.464285714 42
 
< 0.1%
8.035714286 6
 
< 0.1%
8.928571429 290
< 0.1%
13.39285714 84
 
< 0.1%
16.07142857 5
 
< 0.1%
17.85714286 260
< 0.1%
22.32142857 15
 
< 0.1%
24.10714286 3
 
< 0.1%
26.78571429 90
 
< 0.1%
ValueCountFrequency (%)
151800 1
< 0.1%
144659.0909 1
< 0.1%
120436.3636 1
< 0.1%
117559.0909 1
< 0.1%
116945.4545 1
< 0.1%
112818.1818 1
< 0.1%
107636.3636 1
< 0.1%
105600 1
< 0.1%
97545.45455 1
< 0.1%
97454.54545 2
< 0.1%

OC_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202206.57
Minimum202201
Maximum202212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:17.238937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum202201
5-th percentile202201
Q1202204
median202207
Q3202209
95-th percentile202212
Maximum202212
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4045263
Coefficient of variation (CV)1.6836873 × 10-5
Kurtosis-1.1578826
Mean202206.57
Median Absolute Deviation (MAD)3
Skewness-0.0061898714
Sum1.9541243 × 1011
Variance11.590799
MonotonicityNot monotonic
2024-09-26T14:44:17.345348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
202206 102585
10.6%
202209 96453
10.0%
202212 89583
9.3%
202203 84599
8.8%
202207 83813
8.7%
202208 74960
7.8%
202204 74939
7.8%
202202 74173
7.7%
202211 73399
7.6%
202201 73094
7.6%
Other values (2) 138802
14.4%
ValueCountFrequency (%)
202201 73094
7.6%
202202 74173
7.7%
202203 84599
8.8%
202204 74939
7.8%
202205 72591
7.5%
202206 102585
10.6%
202207 83813
8.7%
202208 74960
7.8%
202209 96453
10.0%
202210 66211
6.9%
ValueCountFrequency (%)
202212 89583
9.3%
202211 73399
7.6%
202210 66211
6.9%
202209 96453
10.0%
202208 74960
7.8%
202207 83813
8.7%
202206 102585
10.6%
202205 72591
7.5%
202204 74939
7.8%
202203 84599
8.8%

DISTRIBUTOR_CODE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.1 MiB
DB0110
278245 
DB0209
217421 
DB0706
194044 
DB0652
142181 
DB0655
134509 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5798400
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDB0706
2nd rowDB0706
3rd rowDB0706
4th rowDB0652
5th rowDB0652

Common Values

ValueCountFrequency (%)
DB0110 278245
28.8%
DB0209 217421
22.5%
DB0706 194044
20.1%
DB0652 142181
14.7%
DB0655 134509
13.9%

Length

2024-09-26T14:44:17.460357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:17.585775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
db0110 278245
28.8%
db0209 217421
22.5%
db0706 194044
20.1%
db0652 142181
14.7%
db0655 134509
13.9%

Most occurring characters

ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5798400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1656110
28.6%
D 966400
16.7%
B 966400
16.7%
1 556490
 
9.6%
6 470734
 
8.1%
5 411199
 
7.1%
2 359602
 
6.2%
9 217421
 
3.7%
7 194044
 
3.3%
Distinct18833
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size59.5 MiB
2024-09-26T14:44:17.867223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.5882212
Min length7

Characters and Unicode

Total characters7333257
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)< 0.1%

Sample

1st rowOL144111
2nd rowOL238706
3rd rowOL223076
4th rowOL175529
5th rowOL48851
ValueCountFrequency (%)
ol128896 1289
 
0.1%
ol191061 1277
 
0.1%
ol49938 1243
 
0.1%
ol143966 1223
 
0.1%
ol11104 1114
 
0.1%
ol223486 1089
 
0.1%
ol191033 1085
 
0.1%
ol32854 1080
 
0.1%
ol80887 1048
 
0.1%
ol159815 1035
 
0.1%
Other values (18823) 954917
98.8%
2024-09-26T14:44:18.313295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7333257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 966400
13.2%
L 966400
13.2%
1 879608
12.0%
2 729852
10.0%
3 514930
7.0%
9 513662
7.0%
4 494825
6.7%
6 477333
6.5%
5 467004
6.4%
8 459480
6.3%
Other values (2) 863763
11.8%

CITY
Text

Distinct1679
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.6 MiB
2024-09-26T14:44:18.610418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length8.7443057
Min length3

Characters and Unicode

Total characters8450497
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSedalia
2nd rowWilliston
3rd rowSilver City
4th rowDunkirk
5th rowStroudsburg
ValueCountFrequency (%)
city 32161
 
2.6%
new 14047
 
1.2%
beach 13726
 
1.1%
san 12949
 
1.1%
springs 10660
 
0.9%
fort 10024
 
0.8%
park 9132
 
0.8%
west 8548
 
0.7%
saint 7971
 
0.7%
falls 6906
 
0.6%
Other values (1634) 1090751
89.6%
2024-09-26T14:44:19.096749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8450497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 777071
 
9.2%
e 776788
 
9.2%
n 643293
 
7.6%
o 634555
 
7.5%
r 534447
 
6.3%
l 513454
 
6.1%
i 509793
 
6.0%
t 467095
 
5.5%
s 353589
 
4.2%
250475
 
3.0%
Other values (44) 2989937
35.4%

STATE
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.4 MiB
California
111427 
Illinois
 
56753
Massachusetts
 
47597
Connecticut
 
41359
New York
 
39138
Other values (45)
670126 

Length

Max length14
Median length12
Mean length8.5429739
Min length4

Characters and Unicode

Total characters8255930
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissouri
2nd rowNorth Dakota
3rd rowNew Mexico
4th rowNew York
5th rowPennsylvania

Common Values

ValueCountFrequency (%)
California 111427
 
11.5%
Illinois 56753
 
5.9%
Massachusetts 47597
 
4.9%
Connecticut 41359
 
4.3%
New York 39138
 
4.0%
Alabama 38935
 
4.0%
Florida 38350
 
4.0%
Colorado 30124
 
3.1%
New Jersey 26100
 
2.7%
Arkansas 25677
 
2.7%
Other values (40) 510940
52.9%

Length

2024-09-26T14:44:19.239193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 111427
 
10.1%
new 84237
 
7.6%
illinois 56753
 
5.1%
massachusetts 47597
 
4.3%
connecticut 41359
 
3.8%
york 39138
 
3.6%
alabama 38935
 
3.5%
florida 38350
 
3.5%
colorado 30124
 
2.7%
jersey 26100
 
2.4%
Other values (42) 588071
53.4%

Most occurring characters

ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8255930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1132803
13.7%
i 865262
 
10.5%
n 692063
 
8.4%
o 663356
 
8.0%
s 604292
 
7.3%
e 451896
 
5.5%
r 443948
 
5.4%
l 435199
 
5.3%
t 293046
 
3.5%
C 205185
 
2.5%
Other values (36) 2468880
29.9%

COUNTY
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.9 MiB
City Center
507296 
Dolphin
148154 
Orange
86564 
Santa Cruz
63187 
Scott
50866 
Other values (4)
110333 

Length

Max length11
Median length11
Mean length9.0649369
Min length5

Characters and Unicode

Total characters8760355
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Center
2nd rowCity Center
3rd rowSanta Cruz
4th rowDolphin
5th rowCity Center

Common Values

ValueCountFrequency (%)
City Center 507296
52.5%
Dolphin 148154
 
15.3%
Orange 86564
 
9.0%
Santa Cruz 63187
 
6.5%
Scott 50866
 
5.3%
Silver 40951
 
4.2%
Spencer 31985
 
3.3%
Stephens 21727
 
2.2%
Sumter 15670
 
1.6%

Length

2024-09-26T14:44:19.358257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:19.490880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
city 507296
33.0%
center 507296
33.0%
dolphin 148154
 
9.6%
orange 86564
 
5.6%
santa 63187
 
4.1%
cruz 63187
 
4.1%
scott 50866
 
3.3%
silver 40951
 
2.7%
spencer 31985
 
2.1%
stephens 21727
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8760355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1265201
14.4%
t 1216908
13.9%
C 1077779
12.3%
n 858913
9.8%
r 745653
8.5%
i 696401
7.9%
570483
6.5%
y 507296
 
5.8%
S 224386
 
2.6%
a 212938
 
2.4%
Other values (13) 1384397
15.8%

STREET
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.2 MiB
Str1
201506 
Str4
198939 
Str2
194809 
Str5
194441 
Str3
176705 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3865600
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStr4
2nd rowStr1
3rd rowStr1
4th rowStr3
5th rowStr2

Common Values

ValueCountFrequency (%)
Str1 201506
20.9%
Str4 198939
20.6%
Str2 194809
20.2%
Str5 194441
20.1%
Str3 176705
18.3%

Length

2024-09-26T14:44:19.671494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:19.813690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
str1 201506
20.9%
str4 198939
20.6%
str2 194809
20.2%
str5 194441
20.1%
str3 176705
18.3%

Most occurring characters

ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 966400
25.0%
t 966400
25.0%
r 966400
25.0%
1 201506
 
5.2%
4 198939
 
5.1%
2 194809
 
5.0%
5 194441
 
5.0%
3 176705
 
4.6%
Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2024-09-26T14:44:20.008470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6764800
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPRD0064
2nd rowPRD0113
3rd rowPRD0107
4th rowPRD0147
5th rowPRD0004
ValueCountFrequency (%)
prd0106 107597
 
11.1%
prd0105 51482
 
5.3%
prd0147 43794
 
4.5%
prd0069 33926
 
3.5%
prd0058 31868
 
3.3%
prd0094 30319
 
3.1%
prd0015 29173
 
3.0%
prd0112 27877
 
2.9%
prd0107 26910
 
2.8%
prd0096 26421
 
2.7%
Other values (84) 557033
57.6%
2024-09-26T14:44:20.322227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6764800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1736312
25.7%
P 966400
14.3%
R 966400
14.3%
D 966400
14.3%
1 641564
 
9.5%
6 290649
 
4.3%
5 230585
 
3.4%
9 202083
 
3.0%
2 187942
 
2.8%
8 156096
 
2.3%
Other values (3) 420369
 
6.2%

CATEGORY
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.7 MiB
Soap
251031 
Perfume and Deodrants
224223 
Hair Care
203882 
Lotion
138579 
Kids Care
101069 
Other values (2)
47616 

Length

Max length21
Median length9
Mean length9.9073489
Min length4

Characters and Unicode

Total characters9574462
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHair Care
2nd rowPerfume and Deodrants
3rd rowLotion
4th rowKids Care
5th rowLotion

Common Values

ValueCountFrequency (%)
Soap 251031
26.0%
Perfume and Deodrants 224223
23.2%
Hair Care 203882
21.1%
Lotion 138579
14.3%
Kids Care 101069
10.5%
Dental 47542
 
4.9%
Wipes 74
 
< 0.1%

Length

2024-09-26T14:44:20.455453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:20.570363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
care 304951
17.7%
soap 251031
14.6%
perfume 224223
13.0%
and 224223
13.0%
deodrants 224223
13.0%
hair 203882
11.9%
lotion 138579
8.1%
kids 101069
 
5.9%
dental 47542
 
2.8%
wipes 74
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9574462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1255852
13.1%
e 1025236
10.7%
r 957279
 
10.0%
753397
 
7.9%
o 752412
 
7.9%
n 634567
 
6.6%
d 549515
 
5.7%
i 443604
 
4.6%
t 410344
 
4.3%
s 325366
 
3.4%
Other values (13) 2466890
25.8%

SUBCATEGORY
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 MiB
Shampoo
123413 
Head Lotion
82722 
Soap Gels
68794 
Toilet Soap
68763 
Female Perfume
60317 
Other values (20)
562391 

Length

Max length15
Median length13
Mean length10.878238
Min length3

Characters and Unicode

Total characters10512729
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConditioner
2nd rowUnisex Deodrant
3rd rowBody Lotion
4th rowBaby Cream
5th rowBody Lotion

Common Values

ValueCountFrequency (%)
Shampoo 123413
12.8%
Head Lotion 82722
 
8.6%
Soap Gels 68794
 
7.1%
Toilet Soap 68763
 
7.1%
Female Perfume 60317
 
6.2%
Female Deodrant 58640
 
6.1%
Body Lotion 55857
 
5.8%
Liquid Soap 50112
 
5.2%
Hair Oil 48057
 
5.0%
Medicated Soap 46940
 
4.9%
Other values (15) 302785
31.3%

Length

2024-09-26T14:44:20.710633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
soap 251031
14.8%
shampoo 151515
 
8.9%
lotion 138579
 
8.2%
perfume 125338
 
7.4%
female 118957
 
7.0%
deodrant 98885
 
5.8%
head 82722
 
4.9%
baby 71896
 
4.2%
gels 68794
 
4.0%
toilet 68763
 
4.0%
Other values (17) 523706
30.8%

Most occurring characters

ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10512729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1214507
 
11.6%
e 1169659
 
11.1%
a 1037431
 
9.9%
733786
 
7.0%
i 612885
 
5.8%
t 490496
 
4.7%
m 439604
 
4.2%
p 431719
 
4.1%
d 413868
 
3.9%
S 402546
 
3.8%
Other values (26) 3566228
33.9%

BRAND
Text

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.0 MiB
2024-09-26T14:44:20.943650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length8.1101076
Min length3

Characters and Unicode

Total characters7837608
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowBamboo
2nd rowNavy Blue
3rd rowCoral
4th rowMint
5th rowTan
ValueCountFrequency (%)
shoulders 107597
 
7.6%
hair 107597
 
7.6%
107597
 
7.6%
green 55054
 
3.9%
garnet 51482
 
3.7%
toothy 47412
 
3.4%
mint 43794
 
3.1%
blue 42376
 
3.0%
fuchsia 34059
 
2.4%
arctic 33926
 
2.4%
Other values (91) 778481
55.2%
2024-09-26T14:44:21.363652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7837608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 731404
 
9.3%
r 642306
 
8.2%
a 598243
 
7.6%
i 477346
 
6.1%
o 470321
 
6.0%
442975
 
5.7%
l 394044
 
5.0%
n 391404
 
5.0%
u 378063
 
4.8%
s 327239
 
4.2%
Other values (39) 2984263
38.1%

YEAR
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.2 MiB
2024
640754 
2023
325646 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3865600
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 640754
66.3%
2023 325646
33.7%

Length

2024-09-26T14:44:21.555825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:21.690367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 640754
66.3%
2023 325646
33.7%

Most occurring characters

ValueCountFrequency (%)
2 1932800
50.0%
0 966400
25.0%
4 640754
 
16.6%
3 325646
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1932800
50.0%
0 966400
25.0%
4 640754
 
16.6%
3 325646
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1932800
50.0%
0 966400
25.0%
4 640754
 
16.6%
3 325646
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3865600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1932800
50.0%
0 966400
25.0%
4 640754
 
16.6%
3 325646
 
8.4%

MONTH
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5577276
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:21.822658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3992721
Coefficient of variation (CV)0.51836128
Kurtosis-1.1615238
Mean6.5577276
Median Absolute Deviation (MAD)3
Skewness-0.017029888
Sum6337388
Variance11.555051
MonotonicityNot monotonic
2024-09-26T14:44:21.984944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 90323
9.3%
7 89142
9.2%
9 84176
8.7%
12 81803
8.5%
8 80941
8.4%
5 79524
8.2%
3 78949
8.2%
11 78312
8.1%
4 77778
8.0%
1 76199
7.9%
Other values (2) 149253
15.4%
ValueCountFrequency (%)
1 76199
7.9%
2 73879
7.6%
3 78949
8.2%
4 77778
8.0%
5 79524
8.2%
6 90323
9.3%
7 89142
9.2%
8 80941
8.4%
9 84176
8.7%
10 75374
7.8%
ValueCountFrequency (%)
12 81803
8.5%
11 78312
8.1%
10 75374
7.8%
9 84176
8.7%
8 80941
8.4%
7 89142
9.2%
6 90323
9.3%
5 79524
8.2%
4 77778
8.0%
3 78949
8.2%

DAY
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.405579
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:22.162979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6320286
Coefficient of variation (CV)0.56031833
Kurtosis-1.1645462
Mean15.405579
Median Absolute Deviation (MAD)7
Skewness0.060867129
Sum14887952
Variance74.511917
MonotonicityNot monotonic
2024-09-26T14:44:22.331367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 40600
 
4.2%
8 39399
 
4.1%
22 37408
 
3.9%
19 36714
 
3.8%
12 35493
 
3.7%
21 34942
 
3.6%
9 34474
 
3.6%
14 33884
 
3.5%
11 33076
 
3.4%
10 33070
 
3.4%
Other values (21) 607340
62.8%
ValueCountFrequency (%)
1 31124
3.2%
2 30420
3.1%
3 29215
3.0%
4 29139
3.0%
5 31719
3.3%
6 32819
3.4%
7 40600
4.2%
8 39399
4.1%
9 34474
3.6%
10 33070
3.4%
ValueCountFrequency (%)
31 15524
1.6%
30 25737
2.7%
29 27864
2.9%
28 31243
3.2%
27 26785
2.8%
26 27850
2.9%
25 27850
2.9%
24 30177
3.1%
23 30214
3.1%
22 37408
3.9%

DAY_OF_WEEK
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3241401
Minimum0
Maximum6
Zeros102473
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:22.475219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9529069
Coefficient of variation (CV)0.58749236
Kurtosis-1.1390929
Mean3.3241401
Median Absolute Deviation (MAD)1
Skewness-0.24292582
Sum3212449
Variance3.8138455
MonotonicityNot monotonic
2024-09-26T14:44:22.618384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 166790
17.3%
3 160887
16.6%
5 160520
16.6%
6 160304
16.6%
1 132648
13.7%
0 102473
10.6%
2 82778
8.6%
ValueCountFrequency (%)
0 102473
10.6%
1 132648
13.7%
2 82778
8.6%
3 160887
16.6%
4 166790
17.3%
5 160520
16.6%
6 160304
16.6%
ValueCountFrequency (%)
6 160304
16.6%
5 160520
16.6%
4 166790
17.3%
3 160887
16.6%
2 82778
8.6%
1 132648
13.7%
0 102473
10.6%

QUARTER
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.5 MiB
3
254259 
2
247625 
4
235489 
1
229027 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters966400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

Length

2024-09-26T14:44:22.777508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-26T14:44:22.911317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

Most occurring characters

ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 966400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 966400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 966400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 254259
26.3%
2 247625
25.6%
4 235489
24.4%
1 229027
23.7%

DAY_OF_YEAR
Real number (ℝ)

HIGH CORRELATION 

Distinct303
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.15113
Minimum3
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-09-26T14:44:23.073816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile19
Q197
median186
Q3271
95-th percentile347
Maximum365
Range362
Interquartile range (IQR)174

Descriptive statistics

Standard deviation103.70325
Coefficient of variation (CV)0.56314208
Kurtosis-1.1452495
Mean184.15113
Median Absolute Deviation (MAD)87
Skewness-0.016206668
Sum1.7796366 × 108
Variance10754.364
MonotonicityNot monotonic
2024-09-26T14:44:23.264901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
274 6296
 
0.7%
285 5185
 
0.5%
284 5073
 
0.5%
250 5048
 
0.5%
100 4503
 
0.5%
187 4483
 
0.5%
235 4456
 
0.5%
188 4421
 
0.5%
311 4316
 
0.4%
33 4313
 
0.4%
Other values (293) 918306
95.0%
ValueCountFrequency (%)
3 3400
0.4%
4 3942
0.4%
5 3787
0.4%
6 3263
0.3%
7 3531
0.4%
8 3936
0.4%
10 3209
0.3%
11 3381
0.3%
12 3246
0.3%
13 3049
0.3%
ValueCountFrequency (%)
365 2959
0.3%
364 2057
0.2%
363 2416
0.2%
362 2397
0.2%
361 2886
0.3%
360 2824
0.3%
358 2929
0.3%
357 2795
0.3%
356 2973
0.3%
355 3259
0.3%

Interactions

2024-09-26T14:44:08.235554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:48.606095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:51.172133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:53.445727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.366432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:57.352513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:59.562661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:01.549032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:03.741817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:06.078802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:08.431446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:48.819337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:51.412666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:53.642156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.549396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:57.555469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:59.765872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:01.770369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:04.112257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:06.304549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:08.618318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:49.061749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:51.665718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:53.859089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.740193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:57.815614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:59.945303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:02.021367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:04.301398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:06.533313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:08.870161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:49.300847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:51.913583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:54.060256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.917313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:58.058060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:00.180750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:02.221790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:04.497588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:06.720175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:09.090920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:49.535644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:52.155403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:54.253428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:56.103648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:58.264400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:00.363621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:02.487588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:04.699625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:06.910848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:09.295604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:49.782478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:52.389042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:54.454715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:56.288875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:58.470450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:00.540606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:02.740022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:04.936329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:07.118404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:09.487807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:50.026106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:52.632373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:54.652500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:56.475358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:58.692057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:00.729037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:02.963164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:05.166640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:07.309315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:09.690160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:50.279576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:52.827486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:54.837758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:56.648137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:58.950826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:00.913249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:03.158782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:05.384180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:07.550549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:09.881785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:50.531917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:53.035024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.011176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:56.979970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:59.182698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:01.090091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:03.345722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:05.615206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:07.792821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:10.108143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:50.777414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:53.242463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:55.186284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:57.167109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:43:59.370147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:01.316571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:03.535007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:05.845813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-26T14:44:08.020062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-26T14:44:23.662405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CATEGORYCOUNTYDAYDAY_OF_WEEKDAY_OF_YEARDISTRIBUTOR_CODEMNTH_CODEMONTHOC_CODEQUARTERSALES_PTR_VALUESALES_UNITSSALES_VALUESALES_VOLUMESTATESTREETSUBCATEGORYYEAR
CATEGORY1.0000.0910.0110.0070.0320.0240.0260.0370.0390.0250.0080.0060.0080.0050.0400.0081.0000.026
COUNTY0.0911.0000.0150.0230.0300.0890.0380.0300.0300.0390.0080.0050.0080.0080.0800.0610.1940.038
DAY0.0110.0151.000-0.0400.0920.014-0.0300.0090.0310.0470.0030.0060.0050.0020.0190.0070.0160.071
DAY_OF_WEEK0.0070.023-0.0401.0000.0400.014-0.0570.0430.0390.180-0.0230.010-0.023-0.0200.0520.0200.0170.312
DAY_OF_YEAR0.0320.0300.0920.0401.0000.014-0.3490.9960.9960.9250.0060.046-0.006-0.0160.0120.0070.0540.945
DISTRIBUTOR_CODE0.0240.0890.0140.0140.0141.0000.0110.0140.0140.0120.0080.0050.0080.0070.0890.0330.0710.011
MNTH_CODE0.0260.038-0.030-0.057-0.3490.0111.000-0.349-0.3500.855-0.010-0.020-0.0080.0070.0180.0090.0791.000
MONTH0.0370.0300.0090.0430.9960.014-0.3491.0000.9981.0000.0060.046-0.007-0.0170.0130.0070.0610.987
OC_CODE0.0390.0300.0310.0390.9960.014-0.3500.9981.0000.9810.0060.046-0.007-0.0170.0130.0070.0641.000
QUARTER0.0250.0390.0470.1800.9250.0120.8551.0000.9811.0000.0010.0010.0000.0010.0160.0070.0670.855
SALES_PTR_VALUE0.0080.0080.003-0.0230.0060.008-0.0100.0060.0060.0011.000-0.1260.9900.8940.0270.0050.0120.003
SALES_UNITS0.0060.0050.0060.0100.0460.005-0.0200.0460.0460.001-0.1261.000-0.1320.0670.0050.0050.0120.000
SALES_VALUE0.0080.0080.005-0.023-0.0060.008-0.008-0.007-0.0070.0000.990-0.1321.0000.8850.0250.0050.0110.002
SALES_VOLUME0.0050.0080.002-0.020-0.0160.0070.007-0.017-0.0170.0010.8940.0670.8851.0000.0180.0040.0100.005
STATE0.0400.0800.0190.0520.0120.0890.0180.0130.0130.0160.0270.0050.0250.0181.0000.0980.0480.018
STREET0.0080.0610.0070.0200.0070.0330.0090.0070.0070.0070.0050.0050.0050.0040.0981.0000.0190.009
SUBCATEGORY1.0000.1940.0160.0170.0540.0710.0790.0610.0640.0670.0120.0120.0110.0100.0480.0191.0000.079
YEAR0.0260.0380.0710.3120.9450.0111.0000.9871.0000.8550.0030.0000.0020.0050.0180.0090.0791.000

Missing values

2024-09-26T14:44:10.613416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-26T14:44:12.128857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRANDYEARMONTHDAYDAY_OF_WEEKQUARTERDAY_OF_YEAR
02024012024-01-182024-01-021537.89160.0017121585.454545202201DB0706OL144111SedaliaMissouriCity CenterStr4PRD0064Hair CareConditionerBamboo20241183118
12024012024-01-282024-01-02518.1830.001125518.181818202201DB0706OL238706WillistonNorth DakotaCity CenterStr1PRD0113Perfume and DeodrantsUnisex DeodrantNavy Blue20241286128
22024012024-01-132024-01-02113.6410.000250113.636364202201DB0706OL223076Silver CityNew MexicoSanta CruzStr1PRD0107LotionBody LotionCoral20241135113
32024012024-01-062024-01-02285.71320.000832285.714286202201DB0652OL175529DunkirkNew YorkDolphinStr3PRD0147Kids CareBaby CreamMint202416516
42024012024-01-182024-01-02290.9110.000500290.909091202201DB0652OL48851StroudsburgPennsylvaniaCity CenterStr2PRD0004LotionBody LotionTan20241183118
52024012024-01-182024-01-02873.0030.001350900.000000202201DB0706OL159390NeedlesCaliforniaCity CenterStr1PRD0033SoapToilet SoapTurquoise20241183118
62024012024-01-282024-01-0254.5510.00011054.545455202201DB0706OL239150MercedCaliforniaOrangeStr1PRD0015Kids CareDiapersPearl20241286128
72024012024-01-042024-01-02818.1850.001925818.181818202201DB0706OL237928HarlanKentuckyCity CenterStr5PRD0016Hair CareHair OilMagenta202414314
82024012024-01-052024-01-02142.86160.000416142.857143202201DB0652OL48900AmherstMassachusettsOrangeStr5PRD0147Kids CareBaby CreamMint202415415
92024012024-01-272024-01-0253.57120.00014453.571429202201DB0110OL32584KewaneeIllinoisSilverStr2PRD0105Perfume and DeodrantsFemale PerfumeGarnet20241275127
MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRANDYEARMONTHDAYDAY_OF_WEEKQUARTERDAY_OF_YEAR
9663902023102023-10-252023-10-02285.71320.000832285.714286202210DB0110OL33455ColumbiaPennsylvaniaSanta CruzStr5PRD0069Perfume and DeodrantsFemale DeodrantArctic blue2023102524298
9663912023102023-10-242023-10-021236.3640.0036001236.363636202210DB0652OL96549ApalachicolaFloridaCity CenterStr3PRD0100Perfume and DeodrantsUnisex DeodrantAsh2023102414297
9663922023102023-10-242023-10-021236.3640.0028601236.363636202210DB0706OL207820AmsterdamNew YorkCity CenterStr2PRD0017Perfume and DeodrantsMale PerfumeTangerine2023102414297
9663932023102023-10-292023-10-022127.27120.0030002127.272727202210DB0706OL159390NeedlesCaliforniaCity CenterStr1PRD0122Perfume and DeodrantsFemale DeodrantLilac2023102964302
9663942023102023-10-252023-10-02676.36120.001320676.363636202210DB0110OL12445South San FranciscoCaliforniaCity CenterStr5PRD0015Kids CareDiapersPearl2023102524298
9663952023102023-10-292023-10-02214.29120.000624214.285714202210DB0655OL65099JonesboroughTennesseeSilverStr2PRD0014DentalToothPasteToothy Sensitive2023102964302
9663962023102023-10-282023-10-021527.27120.0030001527.272727202210DB0655OL97239LongviewWashingtonCity CenterStr5PRD0107LotionBody LotionCoral2023102854301
9663972023102023-10-282023-10-02285.71360.000864289.285714202210DB0209OL160211Priest RiverIdahoSanta CruzStr4PRD0147Kids CareBaby CreamMint2023102854301
9663982023102023-10-272023-10-02209.0920.000214209.090909202210DB0655OL113187NapaCaliforniaScottStr1PRD0064Hair CareConditionerBamboo2023102744300
9663992023102023-10-292023-10-02285.71320.000832285.714286202210DB0655OL238263HazletonPennsylvaniaDolphinStr3PRD0069Perfume and DeodrantsFemale DeodrantArctic blue2023102964302

Duplicate rows

Most frequently occurring

MNTH_CODETRANS_DATESTART_DATESALES_VALUESALES_UNITSSALES_VOLUMESALES_PTR_VALUEOC_CODEDISTRIBUTOR_CODEOUTLET_CODECITYSTATECOUNTYSTREETPRODUCT_CODECATEGORYSUBCATEGORYBRANDYEARMONTHDAYDAY_OF_WEEKQUARTERDAY_OF_YEAR# duplicates
02023092023-10-012023-08-288.9310.0000178.928571202209DB0209OL65494ShelbyvilleTennesseeSanta CruzStr5PRD0028SoapToilet SoapIndigo2023101642742
12023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL112602Junction CityKansasSilverStr2PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
22023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL238594DallasTexasSpencerStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
32023092023-10-012023-08-288.9320.0000228.928571202209DB0110OL81664HattiesburgMississippiOrangeStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
42023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL144800Excelsior SpringsMissouriOrangeStr4PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
52023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL222617IndianaPennsylvaniaDolphinStr4PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
62023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL33285Fall RiverMassachusettsCity CenterStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
72023092023-10-012023-08-2813.3930.00003313.392857202209DB0110OL49488AllianceOhioDolphinStr3PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742
82023092023-10-012023-08-2817.8620.00004617.857143202209DB0652OL96577West CovinaCaliforniaCity CenterStr1PRD0027DentalToothPasteToothy Fresh2023101642742
92023092023-10-012023-08-2817.8640.00004417.857143202209DB0110OL80741GlenviewIllinoisScottStr2PRD0105Perfume and DeodrantsFemale PerfumeGarnet2023101642742